[1]} proposed graph convolutional networks (GCN) which work on graph structures. Here, we describe the GCN which is used in our model. We represent a graph \(\mathcal {G}\)  with \(m\)  nodes using an adjacency matrix \(\mathbf {A}\)  of size \(m \times m\) . If there is an edge between node \(i\)  and node \(j\) , then \(A_{ij} = A_{ji} = 1\) . We also add self loops, \(A_{ii} = 1\) , in the graph \(\mathcal {G}\) . We normalize the adjacency matrix \(\mathbf {A}\)  by using symmetric normalization proposed by [1]}. A diagonal node degree matrix \(\mathbf {D}\)  of size \(m \times m\)  is used in the normalization of \(\mathbf {A}\) . \(\text{deg}(v_i)\)  is the number of edges that are connected to the node \(v_i\)  in \(\mathcal {G}\)  and \(\hat{\mathbf {A}}\)  is the corresponding normalized adjacency matrix of \(\mathcal {G}\) . Each node of the graph receives the hidden representation of its neighboring nodes from the \((l-1)\) th layer and uses the following operation to update its own hidden representation.
\(&D^{-\frac{1}{2}}_{ij} = {\left\lbrace \begin{array}{ll}\frac{1}{\sqrt{\text{deg}(v_i)}} & \text{if } i=j\\0 & \text{otherwise}\end{array}\right.}\) 
\(&\hat{\mathbf {A}} = \mathbf {D}^{-\frac{1}{2}} \mathbf {A} \mathbf {D}^{-\frac{1}{2}}\\&\textbf {g}_i^l = \text{ReLU}(\sum _{j=1}^m \hat{A}_{ij} \textbf {W}^l \textbf {g}_j^{l-1})\) 
